Electrical Distribution Substations contain one or more Substation Transformers, which step down the voltage from high transmission line levels (typically 130 kV to 700 kV) to the medium voltage levels (typically from 4 kV to about 35 kV) at which power is distributed to consumers within a distribution service area. At the edge of the Distribution Grid are a number of Service Transformers, which transform the medium voltage of the distribution grid to the low voltages (in the US, typically 120, 208, 240, 277, or 480) required for commercial, industrial, and residential consumers. Other voltages in addition to some of these can be used elsewhere in the world. Each Service Transformer powers one or more metered loads. A metered load can be a dwelling, a commercial or industrial building, an element of municipal infrastructure such as a series of street lamps, agricultural apparatus such as irrigation systems, or any other metered construct which can draw power from the distribution grid, or combinations of these.
Other than the wires connecting a consumer load and the associated meter to a service transformer, the service transformer is the outermost element of the distribution grid before the power is actually delivered to a consumer. A meter is typically attached at the point where the power from the service transformer is delivered to the consumer. Service transformers can be three-phase, or single phase, as can meters. The electrical apparatus included within the power-flow path from a service transformer to the collection of at least one electrical meter is referred to as a Transformer Area Network (TAN). A TAN can have a radial topology, as is common in the US, or it can have a linear or “bus” topology, as is more common in Europe and elsewhere in the world.
Traditionally, reading meters was one of the largest operational costs incurred by electrical utilities. Originally, electric meters were analog devices with an optical read-out that had to be manually examined monthly to drive the utility billing process. Beginning in the 1970s, mechanisms for digitizing meter data and automating its collection began to be deployed. These mechanisms evolved from walk-by or drive-by systems where the meter would broadcast its current reading using a short-range radio signal, which was received by a device carried by the meter reader. These early systems were known as Automated Meter Reading systems or AMRs. Later, a variety of purpose-built data collection networks, commonly employing a combination of short-range RF repeaters in a mesh configuration with collection points equipped with broadband backhaul means for transporting aggregated readings began to be deployed.
These networks were capable of two-way communication between the “metering head-end” at a utility service center and the meters at the edge of this data collection network, and are generally called an Advanced Metering Infrastructure or AMI. AMIs can collect and store readings frequently, typically as often as every 15 minutes, and can report them nearly that often. They can read any meter on demand provided that this feature is used sparingly, and can connect or disconnect any meter on demand as well. AMI meters can pass signals to consumer devices for the purpose of energy conservation, demand management, and variable-rate billing. Because the AMI network is separate from the power distribution grid except for the intersection at the meters, AMI meters are neither aware of nor sensitive to changes in the grid topology or certain conditions on the grid. Nonetheless, the introduction of AMI is often the first step in the direction of a true Smart Grid implementation.
AMI networks generally do not have all the capabilities required to support Smart Grid applications over and above meter reading and demand management. Significantly, the AMI network usually does not use the electrical grid as a transmission medium. It monitors only the metered load points, and hence does not detect electrical changes and conditions elsewhere on the distribution grid. Further, data-carrying capacity from the edge to the central concentrators is typically adequate for meter data and little more. Sophisticated Smart Grid applications for energy conservation, asset protection, non-technical loss detection, load balancing, fault isolation, and recovery management require accurate information about the schematic relationship of grid assets, load and conditions on the several segments of the grid, and the current state of bi-modal and multi-modal assets. This information, together with the geospatial locations of the same assets, is called a grid map and is typically stored in a database. In general, AMI networks have neither the monitoring capability nor the bandwidth to provide these types of information, with the result that present-day grid map databases are seldom updated in real time.
Utilities typically maintain two maps or models of the distribution grid. A Physical Network Model (PNM) aggregates the geospatial location of the assets on the grid. PNMs, thanks to modern GPS technology, are reasonably accurate with respect to point assets such as substations, capacitor banks, transformers, and even individual meters. Inaccuracies stem from failure to update the maps when repairs or changes are made. For example, a service transformer may move from one side of a street to the other as a result of street widening. Such a move may additionally result in a change in the partitioning of metered loads among the service transformers in an area.
Longitudinal assets, especially buried cables, are less well represented in the PNM. The PNM can contain as-designed data regarding the location of the longitudinal assets, but since in many places the cable was laid before global positioning technology had matured, the designs were based on ground-level survey, and the original maps may or may not have been updated to reflect changes. Therefore, the location from the as-designed data may be inaccurate, and subsequent surface changes complicate the problem of verifying the geographic path taken by medium-voltage and low-voltage distribution lines.
The second model is the Logical Network Model, or LNM. LNMs describe how grid components are connected, without reference to their geospatial location. The LNM changes frequently. During the course of repairs, the way transformers attach to taps and laterals, and the way meters attach to transformers, may be altered. Such changes may affect both the LNM and the PNM. In many utilities, such changes are recorded manually by field agents. The manual reports may or may not be updated in the LNM and PNM, and when updates are made the time lag between maintenance occurring and its being recorded could be significant.
The “last mile” problem of grid mapping involves determining what service transformer, and what phase or phases of the service transformer in cases where the transformer is multi phase, powers a particular meter. In locales where transformers are mounted on poles and tap lines are above ground, one might think this would be obvious. However, in those locales, it is very easy after an outage caused by a storm, a traffic accident, or scheduled construction, for repairs to be made in such a way as to change the transformer to which a meter is attached. In dense neighborhoods it is not always apparent how bundled and criss-crossing power lines connect buildings to transformers, especially when multiple transformers are attached to one pole.
In cases where transformers are pad-mounted or underground, and taps run underground, the construction may pre-date grid mapping. In that case, the only data that may be available are schematic designs made by survey. In general, no reliable record exists of whether the “last mile” of the grid was built strictly according to specification, or what has been the effect of subsequent modifications. When the taps are not visible, obtaining an accurate map of a TAN is labor intensive at best.
Practical benefits of having accurate TAN-level grid maps are asset protection and theft detection. The two go somewhat hand in hand, as unmetered power theft is a cause, though not the only cause, of transformer overload. What is needed is an accurate TAN map to help firmly establish that all power drawn from a transformer is metered, and to allow for large unpredictable loads, such as quick-charging electric vehicle batteries, to be arbitrated. An accurate TAN map is also helpful in planning and ordering repairs, upgrades, and modifications at the edge of the distribution grid